Introduction

The purpose of this Markdown document is to utilized some of the famous R htmlwidgets. In this document the Plotly and Highcharacter R visualization packages were used. The ggplot2’s diamonds dataset, which is adataset containing the prices and other attributes of almost 54,000 diamonds, was incorporated in this visualization. For the cookbook refer to: http://ggplot2.tidyverse.org/reference/diamonds.html

Loading Packages and Dataset

The dataset is sample size of 1000 diamonds randomly selected.

library(highcharter)
library(plotly)
library(ggplot2)

set.seed(200)
data <- diamonds[sample(nrow(diamonds), 1000),]

Visualization:

Figure 1, presents the correlation between carat measure in the price of a diamond. This plot was fabricated by plotly.

plot_ly(data, x = ~carat, y = ~price, color = ~carat,
        size = ~carat, text = ~paste("Clarity: ", clarity))%>%
        layout(autosize = F, width = 800, height = 500)

Figure 1:

Figure 2, presents the information in Figure 1. However, the plot was created in ggplot and then converted to a plotly graph by ggplotly function.

fig2 <- ggplot(data, aes(x = carat, y = price)) +
        geom_point(aes(text = paste("Clarity:", clarity),fill=cut,color=cut))+
        theme_classic() 

ggplotly(fig2)%>%
  layout(autosize = F, width = 800, height = 500)

Figure 2:







Figure 3 was also created by ggplot first and then converted to plotly. The graph presents Figures 1 and 2, but grouped by the quality of diamond cut.

fig3 <- ggplot(data, aes(x = carat, y = price)) + geom_point(aes(text = paste("Clarity:", clarity))) +
        geom_smooth(aes(colour = cut, fill = cut)) + facet_wrap(~ cut) 

ggplotly(fig3)%>%
  layout(autosize = F, width = 800, height = 500)

Figure 3:







Figure 4 was also created by ggplot first and then converted to plotly. This graph presents previous figures in Histogram form.

fig4 <- ggplot(data,aes(x=cut,fill=clarity))+geom_bar(position="dodge")

ggplotly(fig4)%>%
  layout(autosize = F, width = 800, height = 500)

Figure 4: